# Ultralytics YOLO 🚀, GPL-3.0 license

import contextlib
import hashlib
import json
import os
import subprocess
import time
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import is_tarfile
from zipfile import is_zipfile

import cv2
import numpy as np
from PIL import ExifTags, Image, ImageOps
from tqdm import tqdm

from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, colorstr, emojis, yaml_load
from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
from ultralytics.yolo.utils.downloads import download, safe_download, unzip_file
from ultralytics.yolo.utils.ops import segments2boxes

HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # include image suffixes
VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true'  # global pin_memory for dataloaders
IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation

# Get orientation exif tag
for orientation in ExifTags.TAGS.keys():
    if ExifTags.TAGS[orientation] == 'Orientation':
        break


def img2label_paths(img_paths):
    # Define label paths as a function of image paths
    sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}'  # /images/, /labels/ substrings
    return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]


def get_hash(paths):
    # Returns a single hash value of a list of paths (files or dirs)
    size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
    h = hashlib.sha256(str(size).encode())  # hash sizes
    h.update(''.join(paths).encode())  # hash paths
    return h.hexdigest()  # return hash


def exif_size(img):
    # Returns exif-corrected PIL size
    s = img.size  # (width, height)
    with contextlib.suppress(Exception):
        rotation = dict(img._getexif().items())[orientation]
        if rotation in [6, 8]:  # rotation 270 or 90
            s = (s[1], s[0])
    return s


def verify_image_label(args):
    # Verify one image-label pair
    im_file, lb_file, prefix, keypoint, num_cls = args
    # number (missing, found, empty, corrupt), message, segments, keypoints
    nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, '', [], None
    try:
        # verify images
        im = Image.open(im_file)
        im.verify()  # PIL verify
        shape = exif_size(im)  # image size
        shape = (shape[1], shape[0])  # hw
        assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
        assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
        if im.format.lower() in ('jpg', 'jpeg'):
            with open(im_file, 'rb') as f:
                f.seek(-2, 2)
                if f.read() != b'\xff\xd9':  # corrupt JPEG
                    ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
                    msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'

        # verify labels
        if os.path.isfile(lb_file):
            nf = 1  # label found
            with open(lb_file) as f:
                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
                if any(len(x) > 6 for x in lb) and (not keypoint):  # is segment
                    classes = np.array([x[0] for x in lb], dtype=np.float32)
                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
                lb = np.array(lb, dtype=np.float32)
            nl = len(lb)
            if nl:
                if keypoint:
                    assert lb.shape[1] == 56, 'labels require 56 columns each'
                    assert (lb[:, 5::3] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
                    assert (lb[:, 6::3] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
                    kpts = np.zeros((lb.shape[0], 39))
                    for i in range(len(lb)):
                        kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5, 3))  # remove occlusion param from GT
                        kpts[i] = np.hstack((lb[i, :5], kpt))
                    lb = kpts
                    assert lb.shape[1] == 39, 'labels require 39 columns each after removing occlusion parameter'
                else:
                    assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
                    assert (lb[:, 1:] <= 1).all(), \
                        f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
                # All labels
                max_cls = int(lb[:, 0].max())  # max label count
                assert max_cls <= num_cls, \
                    f'Label class {max_cls} exceeds dataset class count {num_cls}. ' \
                    f'Possible class labels are 0-{num_cls - 1}'
                assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
                _, i = np.unique(lb, axis=0, return_index=True)
                if len(i) < nl:  # duplicate row check
                    lb = lb[i]  # remove duplicates
                    if segments:
                        segments = [segments[x] for x in i]
                    msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed'
            else:
                ne = 1  # label empty
                lb = np.zeros((0, 39), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
        else:
            nm = 1  # label missing
            lb = np.zeros((0, 39), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
        if keypoint:
            keypoints = lb[:, 5:].reshape(-1, 17, 2)
        lb = lb[:, :5]
        return im_file, lb, shape, segments, keypoints, nm, nf, ne, nc, msg
    except Exception as e:
        nc = 1
        msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}'
        return [None, None, None, None, None, nm, nf, ne, nc, msg]


def polygon2mask(imgsz, polygons, color=1, downsample_ratio=1):
    """
    Args:
        imgsz (tuple): The image size.
        polygons (np.ndarray): [N, M], N is the number of polygons, M is the number of points(Be divided by 2).
        color (int): color
        downsample_ratio (int): downsample ratio
    """
    mask = np.zeros(imgsz, dtype=np.uint8)
    polygons = np.asarray(polygons)
    polygons = polygons.astype(np.int32)
    shape = polygons.shape
    polygons = polygons.reshape(shape[0], -1, 2)
    cv2.fillPoly(mask, polygons, color=color)
    nh, nw = (imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio)
    # NOTE: fillPoly firstly then resize is trying the keep the same way
    # of loss calculation when mask-ratio=1.
    mask = cv2.resize(mask, (nw, nh))
    return mask


def polygons2masks(imgsz, polygons, color, downsample_ratio=1):
    """
    Args:
        imgsz (tuple): The image size.
        polygons (list[np.ndarray]): each polygon is [N, M], N is number of polygons, M is number of points (M % 2 = 0)
        color (int): color
        downsample_ratio (int): downsample ratio
    """
    masks = []
    for si in range(len(polygons)):
        mask = polygon2mask(imgsz, [polygons[si].reshape(-1)], color, downsample_ratio)
        masks.append(mask)
    return np.array(masks)


def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
    """Return a (640, 640) overlap mask."""
    masks = np.zeros((imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio),
                     dtype=np.int32 if len(segments) > 255 else np.uint8)
    areas = []
    ms = []
    for si in range(len(segments)):
        mask = polygon2mask(imgsz, [segments[si].reshape(-1)], downsample_ratio=downsample_ratio, color=1)
        ms.append(mask)
        areas.append(mask.sum())
    areas = np.asarray(areas)
    index = np.argsort(-areas)
    ms = np.array(ms)[index]
    for i in range(len(segments)):
        mask = ms[i] * (i + 1)
        masks = masks + mask
        masks = np.clip(masks, a_min=0, a_max=i + 1)
    return masks, index


def check_det_dataset(dataset, autodownload=True):
    # Download, check and/or unzip dataset if not found locally
    data = check_file(dataset)

    # Download (optional)
    extract_dir = ''
    if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
        new_dir = safe_download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False)
        data = next((DATASETS_DIR / new_dir).rglob('*.yaml'))
        extract_dir, autodownload = data.parent, False

    # Read yaml (optional)
    if isinstance(data, (str, Path)):
        data = yaml_load(data, append_filename=True)  # dictionary

    # Checks
    for k in 'train', 'val', 'names':
        if k not in data:
            raise SyntaxError(
                emojis(f"{dataset} '{k}:' key missing ❌.\n"
                       f"'train', 'val' and 'names' are required in data.yaml files."))
    if isinstance(data['names'], (list, tuple)):  # old array format
        data['names'] = dict(enumerate(data['names']))  # convert to dict
    data['nc'] = len(data['names'])

    # Resolve paths
    path = Path(extract_dir or data.get('path') or Path(data.get('yaml_file', '')).parent)  # dataset root

    if not path.is_absolute():
        path = (DATASETS_DIR / path).resolve()
        data['path'] = path  # download scripts
    for k in 'train', 'val', 'test':
        if data.get(k):  # prepend path
            if isinstance(data[k], str):
                x = (path / data[k]).resolve()
                if not x.exists() and data[k].startswith('../'):
                    x = (path / data[k][3:]).resolve()
                data[k] = str(x)
            else:
                data[k] = [str((path / x).resolve()) for x in data[k]]

    # Parse yaml
    train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))
    if val:
        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
        if not all(x.exists() for x in val):
            msg = f"\nDataset '{dataset}' not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()]
            if s and autodownload:
                LOGGER.warning(msg)
            else:
                raise FileNotFoundError(msg)
            t = time.time()
            if s.startswith('http') and s.endswith('.zip'):  # URL
                safe_download(url=s, dir=DATASETS_DIR, delete=True)
                r = None  # success
            elif s.startswith('bash '):  # bash script
                LOGGER.info(f'Running {s} ...')
                r = os.system(s)
            else:  # python script
                r = exec(s, {'yaml': data})  # return None
            dt = f'({round(time.time() - t, 1)}s)'
            s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f'failure {dt} ❌'
            LOGGER.info(f'Dataset download {s}\n')
    check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf')  # download fonts

    return data  # dictionary


def check_cls_dataset(dataset: str):
    """
    Check a classification dataset such as Imagenet.

    Copy code
    This function takes a `dataset` name as input and returns a dictionary containing information about the dataset.
    If the dataset is not found, it attempts to download the dataset from the internet and save it to the local file system.

    Args:
        dataset (str): Name of the dataset.

    Returns:
        data (dict): A dictionary containing the following keys and values:
            'train': Path object for the directory containing the training set of the dataset
            'val': Path object for the directory containing the validation set of the dataset
            'nc': Number of classes in the dataset
            'names': List of class names in the dataset
    """
    data_dir = (DATASETS_DIR / dataset).resolve()
    if not data_dir.is_dir():
        LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
        t = time.time()
        if dataset == 'imagenet':
            subprocess.run(f"bash {ROOT / 'yolo/data/scripts/get_imagenet.sh'}", shell=True, check=True)
        else:
            url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
            download(url, dir=data_dir.parent)
        s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
        LOGGER.info(s)
    train_set = data_dir / 'train'
    test_set = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val'  # data/test or data/val
    nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()])  # number of classes
    names = [x.name for x in (data_dir / 'train').iterdir() if x.is_dir()]  # class names list
    names = dict(enumerate(sorted(names)))
    return {'train': train_set, 'val': test_set, 'nc': nc, 'names': names}


class HUBDatasetStats():
    """ Class for generating HUB dataset JSON and `-hub` dataset directory

    Arguments
        path:           Path to data.yaml or data.zip (with data.yaml inside data.zip)
        autodownload:   Attempt to download dataset if not found locally

    Usage
        from ultralytics.yolo.data.utils import HUBDatasetStats
        stats = HUBDatasetStats('coco128.yaml', autodownload=True)  # usage 1
        stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco6.zip')  # usage 2
        stats.get_json(save=False)
        stats.process_images()
    """

    def __init__(self, path='coco128.yaml', autodownload=False):
        # Initialize class
        zipped, data_dir, yaml_path = self._unzip(Path(path))
        try:
            # data = yaml_load(check_yaml(yaml_path))  # data dict
            data = check_det_dataset(yaml_path, autodownload)  # data dict
            if zipped:
                data['path'] = data_dir
        except Exception as e:
            raise Exception('error/HUB/dataset_stats/yaml_load') from e

        self.hub_dir = Path(str(data['path']) + '-hub')
        self.im_dir = self.hub_dir / 'images'
        self.im_dir.mkdir(parents=True, exist_ok=True)  # makes /images
        self.stats = {'nc': len(data['names']), 'names': list(data['names'].values())}  # statistics dictionary
        self.data = data

    @staticmethod
    def _find_yaml(dir):
        # Return data.yaml file
        files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml'))  # try root level first and then recursive
        assert files, f'No *.yaml file found in {dir}'
        if len(files) > 1:
            files = [f for f in files if f.stem == dir.stem]  # prefer *.yaml files that match dir name
            assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed'
        assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}'
        return files[0]

    def _unzip(self, path):
        # Unzip data.zip
        if not str(path).endswith('.zip'):  # path is data.yaml
            return False, None, path
        assert Path(path).is_file(), f'Error unzipping {path}, file not found'
        unzip_file(path, path=path.parent)
        dir = path.with_suffix('')  # dataset directory == zip name
        assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/'
        return True, str(dir), self._find_yaml(dir)  # zipped, data_dir, yaml_path

    def _hub_ops(self, f, max_dim=1920):
        # HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing
        f_new = self.im_dir / Path(f).name  # dataset-hub image filename
        try:  # use PIL
            im = Image.open(f)
            r = max_dim / max(im.height, im.width)  # ratio
            if r < 1.0:  # image too large
                im = im.resize((int(im.width * r), int(im.height * r)))
            im.save(f_new, 'JPEG', quality=50, optimize=True)  # save
        except Exception as e:  # use OpenCV
            LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}')
            im = cv2.imread(f)
            im_height, im_width = im.shape[:2]
            r = max_dim / max(im_height, im_width)  # ratio
            if r < 1.0:  # image too large
                im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)
            cv2.imwrite(str(f_new), im)

    def get_json(self, save=False, verbose=False):
        # Return dataset JSON for Ultralytics HUB
        # from ultralytics.yolo.data import YOLODataset
        from ultralytics.yolo.data.dataloaders.v5loader import LoadImagesAndLabels

        def _round(labels):
            # Update labels to integer class and 6 decimal place floats
            return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]

        for split in 'train', 'val', 'test':
            if self.data.get(split) is None:
                self.stats[split] = None  # i.e. no test set
                continue
            dataset = LoadImagesAndLabels(self.data[split])  # load dataset
            x = np.array([
                np.bincount(label[:, 0].astype(int), minlength=self.data['nc'])
                for label in tqdm(dataset.labels, total=len(dataset), desc='Statistics')])  # shape(128x80)
            self.stats[split] = {
                'instance_stats': {
                    'total': int(x.sum()),
                    'per_class': x.sum(0).tolist()},
                'image_stats': {
                    'total': len(dataset),
                    'unlabelled': int(np.all(x == 0, 1).sum()),
                    'per_class': (x > 0).sum(0).tolist()},
                'labels': [{
                    str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)]}

        # Save, print and return
        if save:
            stats_path = self.hub_dir / 'stats.json'
            LOGGER.info(f'Saving {stats_path.resolve()}...')
            with open(stats_path, 'w') as f:
                json.dump(self.stats, f)  # save stats.json
        if verbose:
            LOGGER.info(json.dumps(self.stats, indent=2, sort_keys=False))
        return self.stats

    def process_images(self):
        # Compress images for Ultralytics HUB
        # from ultralytics.yolo.data import YOLODataset
        from ultralytics.yolo.data.dataloaders.v5loader import LoadImagesAndLabels

        for split in 'train', 'val', 'test':
            if self.data.get(split) is None:
                continue
            dataset = LoadImagesAndLabels(self.data[split])  # load dataset
            with ThreadPool(NUM_THREADS) as pool:
                for _ in tqdm(pool.imap(self._hub_ops, dataset.im_files), total=len(dataset), desc=f'{split} images'):
                    pass
        LOGGER.info(f'Done. All images saved to {self.im_dir}')
        return self.im_dir
